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Title:
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A FUZZY WEB ANALYTICS MODEL FOR WEB MINING |
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Author(s):
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Darius Zumstein , Michael Kaufmann |
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ISBN:
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978-972-8924-88-1 |
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Editors:
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Ajith P. Abraham |
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Year:
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2009 |
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Edition:
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Single |
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Keywords:
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Fuzzy classification, fuzzy logic, web analytics, web metrics, web usage mining, electronic business. |
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Type:
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Full Paper |
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First Page:
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59 |
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Last Page:
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66 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Analysis of web data and metrics has become a crucial task of electronic business to monitor and optimize websites, their
usage and online marketing. First, this paper shows an overview of the use of web analytics, different web metrics
measured by web analytics software like Google Analytics and other Key Performance Indicators (KPIs) of e-business.
Second, an architecture of a fuzzy web analytics model for web usage mining is proposed to measure, analyze and
improve website traffic and success. In a fuzzy classification, values of web data and metrics can be classified into several
classes at the same time, and it allows gradual ranking within classes. Therefore, the fuzzy logic approach enables a more
precise classification and segmentation of web metrics and the use of linguistic variables or terms, represented by
membership functions. Third, a fuzzy data warehouse as a promising web usage mining tool allows fuzzy dicing, slicing
and (dis)aggregation, and the definition of new query concepts like many page views, high traffic period or very
loyal visitors. Fourth, Inductive Fuzzy Classification (IFC) enables a automated definition of membership functions
using induction. This inferred membership degrees can be used for analysis and reporting. |
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